جست‌وجوی استاد و پژوهشگر

Ingo Scholtesمشاهده پروفایل

استاد

• Machine Learning
• Network Science
• Graph Mining
• +۵ مورد دیگر

Prof. Ingo Scholtes is a Full Professor of Machine Learning for Complex Networks at Julius-Maximilians-Universität Würzburg's Center for Artificial Intelligence and Data Science (CAIDAS). He holds a doctorate in computer science and mathematics from the University of Trier and has held roles including SNSF Professor at the University of Zurich, Full Professor at Bergische Universität Wuppertal, and Senior Assistant at ETH Zürich. His research focuses on higher-order graph analytics for temporal networks, machine learning, and computational social science. Education: PhD in Computer Science (University of Trier, Germany), Postdoctoral Research at ETH Zürich (2011–2016), and prior roles at Karlsruhe Institute of Technology and CERN. Research Interests: Machine learning on graphs, temporal network analysis, higher-order network models, and their applications in software engineering and social systems. He develops open-source tools like pathpy and git2net for network analysis. Recent Work: Focuses on causality-aware graph neural networks, temporal graph isomorphism, and network science applications in AI. Recent articles include studies on temporal network dynamics, path prediction, and Bayesian inference of network transitions. Awards: SNSF Professorship (2018), Junior-Fellowship (2014), and German Academic Scholarship Foundation (2004-2005). Active in editorial roles for EPJ Data Science and leadership in GI's Computational Social Science working group.

• Combinatorial Optimization
• Artificial Intelligence
• Algorithms and Heuristics
• +۳ مورد دیگر

Eric Taillard is a Full Professor and Head of the Cross-functional Skills Group 'Optimization & Sustainability' at the School of Engineering and Management of the Canton of Vaud (HES-SO), part of the University of Applied Sciences and Arts Western Switzerland (HES-SO). He holds degrees in Computer Science, Engineering, and Production Management from HES-SO institutions. His research focuses on combinatorial optimization, metaheuristics, and algorithm design, with notable contributions to the Travelling Salesman Problem (TSP) and automated map labeling. Education: BSc in Computer Science and Communication Systems MSc in Engineering (Algorithms and Data Structures) MA in Production and Logistics Management Key Projects: Big Data in Combinatorial Optimization (2017–2020): Explored optimization techniques for large-scale problems. Parallel Computing on GPU Clusters (2010–2012): Enhanced combinatorial optimization via GPU parallelization. Automated Label Placement (2007–2008): Developed the PAL library for efficient cartographic labeling. Research Interests: Metaheuristics, TSP algorithms, POPMUSIC framework, algorithmic efficiency, and applications in logistics and cartography. His work has been published in journals like the EURO Journal of Operational Research and presented at Metaheuristics International Conferences. He leads interdisciplinary teams and collaborates with academic and industrial partners globally.

• Machine Learning
• Data Mining
• Artificial Intelligence
• +۷ مورد دیگر

Prof. Alexandros Kalousis holds the position of Ordinarius HES Professor at the Geneva School of Economics and Management (HES-SO). His primary affiliation is within the Department of Management Information Systems under the School of Economics and Services. His research focuses on machine learning, data mining, and their applications in biomedical systems, cybersecurity, and generative modeling. Key projects include SimGait (SNSF-funded), which develops neuromechanical models for pathological gait analysis using machine learning, and RAWFIE (EU-funded), creating a mixed network testbed for autonomous vehicle experimentation. He has also led projects on time series forecasting, olfactory modeling for perfume creation, and metric/kernel learning optimization. Notable contributions span graph generative models like DGAE and GLAD, cybersecurity implications of LLMs, and reproducible indoor positioning systems. His work integrates theory-driven models with deep learning, emphasizing interpretability and extrapolation capabilities. Research collaborations include EPFL's Biorobotics Lab, Geneva University Hospitals, and industry partners like Firmenich. Total funding exceeds CHF 3M across projects like SimGait (CHF 2.1M) and RAWFIE (CHF 623K).

Mateo Dujićمشاهده پروفایل

پژوهشگر

• Graph Algorithms
• Directed Acyclic Graphs (DAGs)
• Software Heritage Graph
• +۲ مورد دیگر

Mateo Dujić is a PhD student at the Database Technology Group (DBTG) of the Department of Informatics, University of Zürich, joining in September 2023. Advisors: Prof. Dr. Michael Böhlen and Prof. Dr. Sven Helmer Research Focus: Development of algorithms for Directed Acyclic Graphs (DAGs), with a core application in the Software Heritage Graph , emphasizing graph traversal and optimization techniques Teaching: Lecturer for Database Systems (Spring 2024 and Spring 2025) Education: B.Sc. in Mathematics (University of Zagreb, 2021), followed by an M.Sc. in Computer Science and Mathematics (University of Zagreb, 2023). His work intersects computer science and mathematics , with technical expertise in graph theory and database systems .

Haozhe Zhangمشاهده پروفایل

پژوهشگر

• Database Systems
• Incremental View Maintenance
• Cardinality Estimation
• +۴ مورد دیگر

Haozhe Zhang is a Postdoctoral Researcher in the Data Systems and Theory (DaST) group at the Department of Informatics, University of Zurich, supervised by Prof. Dan Olteanu. His career bridges theoretical research and practical implementation in database systems. Education: DPhil in Computer Science, University of Oxford (2023) MSc in Computer Science, University of Oxford (2017) BSc in Computer Science, University of Nottingham (2016) Research Focus: Haozhe's work centers on database theory, emphasizing incremental view maintenance and cardinality estimation . His research explores efficient algorithms for dynamic relational data, theoretical foundations of conjunctive queries, and robust cardinality estimation techniques like LpBound. Publications & Trends: His contributions include theoretical analyses of conjunctive queries under updates, practical systems like F-IVM for analytics over evolving data, and worst-case optimal algorithms for triangle counting. Recent work at SIGMOD 2025 and ICDT/AMW workshops highlights advancements in dynamic query evaluation and cardinality estimation guarantees. Scientific Recognition: Best Paper Award, SIGMOD 2025 Best Paper Award, ICDT 2019 Teaching Contributions: Instructor, Foundations of Data Sciences (UZH, Fall 2024) Teaching Assistant for Foundations of Data Sciences (UZH, Fall 2020–Fall 2023), Efficient Algorithms (UZH, Spring 2021–Spring 2025), and Modern Data Analytics (UZH, Fall 2023).

• Artificial Intelligence
• Machine Learning
• Data Science
• +۴ مورد دیگر

Dr. Alessia PANNESE is an Associate Professor at the University of Milan , affiliated with the College of Science and the Department of Computer Science . Her expertise spans Artificial Intelligence, Machine Learning, and Data Science. Fields of Interest: Artificial Intelligence, Machine Learning, Data Science Key Research: Multi-modal data integration, graph-based knowledge discovery Her recent publications focus on deep learning architectures , big data algorithms , and graph analytics , reflecting interdisciplinary applications in AI and database systems. Notable accolades include the ACM Best Paper Award (2020) and IEEE Rising Star Award (2021) . She advises PhD candidates in data-driven research and leads the Data Intelligence Lab , specializing in real-time analytics and scalable solutions.

• Economics
• Innovation Economics
• R&D Networks
• +۴ مورد دیگر

Michael David König is a Lecturer at the Department of Management, Technology, and Economics at ETH Zürich, specializing in Innovation Economics within the KOF Swiss Economic Institute. His research focuses on the intersection of network theory and economics, particularly examining R&D networks, technology spillovers, and innovation dynamics. König maintains an active research profile with publications spanning economics, network science, and computer science. König's research spans multiple domains including economic network analysis, innovation economics, and technology diffusion. He has made significant contributions to understanding how firms form R&D collaborations and how knowledge flows through these networks. His work combines theoretical modeling with empirical analysis of large-scale network data, revealing patterns such as oscillatory dynamics in R&D collaboration intensity. Recent research has also addressed practical economic issues, including firm responses to the COVID-19 pandemic and factors influencing R&D investment decisions in Switzerland. His interdisciplinary approach bridges economics with computational methods, reflecting his background in both theoretical and applied network analysis. König's publication record demonstrates a strong interdisciplinary trajectory, beginning with contributions to wireless network protocols and distributed systems before focusing more intensively on economic applications of network theory. A consistent theme across his career has been the study of how networks evolve and how these structures influence outcomes in various domains, from technology diffusion to economic fluctuations. His research often employs sophisticated modeling techniques to analyze the coevolution of networks and economic behavior, with particular attention to the dynamics of knowledge creation and diffusion. König teaches Introduction to Microeconomics at ETH Zürich, as evidenced by his listing in the Autumn Semester 2025 course catalog. His office is located at LEE G 224, Leonhardstrasse 21, 8092 Zürich, Switzerland. He is affiliated with the KOF Innovation Economics research group, which focuses on innovation, technological change, and their economic implications.

Rui Yaoمشاهده پروفایل

مدرس

• Large-scale equilibrium modeling in multi-modal transport systems
• Individual mobility choice modeling
• Demand management strategies
• +۳ مورد دیگر

Rui Yao is a Lecturer at École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the College of Engineering (ENAC) and the Department of Civil Engineering. Additionally, he works as a Scientist in the Laboratory for Human-Oriented Mobility Eco-system (HOMES) within EPFL. His research focuses on large-scale equilibrium modeling in multi-modal transport systems, individual mobility choice modeling, and demand management strategies. Education: B.Sc. in Civil Engineering, Technion – Israel Institute of Technology Direct-track Ph.D. in Transportation Engineering, Technion – Israel Institute of Technology His research spans both theoretical and applied domains, including stochastic traffic equilibrium , multi-passenger ridesharing systems , perturbed utility models , and deep learning for choice analysis . He has contributed to advancements in data-driven route choice modeling , integrated equilibrium models for electrified logistics , and stable matching frameworks for mobility platforms . Rui Yao is affiliated with the Human-Oriented Mobility Eco-system (HOMES) lab at EPFL, which focuses on innovative transportation solutions. His work bridges theoretical modeling with real-world applications in smart mobility and transportation policy.

Matthias Grossglauserمشاهده پروفایل

استاد

• Machine Learning
• Stochastic Networks
• Discrete Choice Models
• +۴ مورد دیگر

Matthias Grossglauser is a Full Professor at the School of Computer and Communication Sciences at École Polytechnique Fédérale de Lausanne (EPFL), where he co-directs the Information and Network Dynamics lab. He serves on the Federal Communications Commission (ComCom), Switzerland's telecommunications regulatory authority, and previously directed EPFL's Doctoral School in Computer and Communication Sciences (2016-2019). His career includes positions at Nokia Research Center (Internet Laboratory lead), AT&T Research, and EPFL (Assistant Professor). Education Ph.D. in Computer Science from Sorbonne Universités M.Sc. in Electrical Engineering from Georgia Institute of Technology Engineering degree in Communication Systems from EPFL Research Focus Grossglauser's research integrates machine learning, stochastic networks, and discrete choice models to address challenges in artificial intelligence, network science, computational social sciences, and recommender systems. His work emphasizes both theoretical foundations and practical applications, including political forecasting, climate communication, and network dynamics. Publication Trends Recent articles demonstrate strong focus on causal inference, optimal learning algorithms, and social network analysis. Dominant themes include reinforcement learning optimization, graph-based modeling, and NLP applications in political science. Methodological innovations in matrix factorization, Bayesian modeling, and stochastic processes recur throughout. Awards & Honors Fellow of IEEE and ELLIS Cor Baayen Award (1998) CoNEXT/SIGCOMM Rising Star Award (2006) Best Paper Awards: ACM COSN (2014), IEEE INFOCOM (2001) Nokia Mobile Data Challenge Winner (2012) Academic Leadership Has advised 16+ PhD students to completion and currently supervises 4 doctoral candidates. Secured research funding for projects including dynamic recommender systems, network alignment algorithms, and computational social science tools (e.g., Predikon.ch vote prediction platform). Leads the Information and Network Dynamics lab, focusing on AI-driven network analysis.

• Data Science
• Big Data
• Natural Language Query Processing
• +۴ مورد دیگر

Prof. Dr. Kurt Stockinger is a Professor of Computer Science at ZHAW School of Engineering and holds a doctorate at the University of Zurich . He serves as Head of the MAS Data Science program and co-leads the ZHAW Datalab . His research focuses on Intelligent Information Systems , bridging information systems, natural language processing, and machine learning. Affiliated with the University of Zurich, he contributes to Quantum Machine Learning and Open Data Exploration initiatives. Stockinger's educational background includes a PhD in Computer Science (University of Vienna & CERN), a Master in Business Informatics (University of Vienna), and a CAS in Didactics & Methodology (ZHAW). He has taught courses in Quantum Computing , Big Data for Natural Sciences , and Data Science programs at ZHAW and University of Zurich. His research spans Data Science , Big Data , Natural Language Query Processing , Knowledge Graphs , and Quantum Machine Learning . Recent publications focus on quantum autoencoders , hybrid quantum neural networks , and prompt engineering for knowledge graph question answering. He has developed frameworks like ScienceBenchmark for real-world NL-to-SQL evaluation and NQuest for natural language query exploration. Scientific awards include the Best Paper Award at 7th Swiss Conference on Data Science (2020) He leads major projects such as DataGEMS (Data Discovery Platform, Horizon Europe) Digital Health Zurich (Clinical Innovation Lab) INODE4StatBot.swiss (NL-to-SQL Translation) GraphQueryML (Graph Database Optimization) ScienceBenchmark (NL-to-SQL Evaluation) Stockinger's work intersects with computer vision , biomedical data , and industrial applications , demonstrated through collaborations with institutions like Lawrence Berkeley National Laboratory, CERN, and University of Washington. He has contributed to establishing QuantumBasel and ZHAW Datalab as research hubs.

Anastasios Kouvelasمشاهده پروفایل

مدرس

• Traffic flow theory
• System and control theory
• Optimization
• +۱۰ مورد دیگر

Dr. Anastasios Kouvelas is a Lecturer at ETH Zurich, where he serves as head of the Road Traffic Engineering research group at the Institute of Transport Planning and Systems (IVT), Department of Civil, Environmental and Geomatic Engineering. He has held this position since August 2018, succeeding Dr. Monica Menendez who moved to New York University in Abu Dhabi. Prior to joining ETH Zurich, he was a research associate at the Urban Transport Systems Laboratory (LUTS) at EPFL (2014-2018) and a postdoctoral fellow at Partners for Advanced Transportation Technology (PATH) at the University of California, Berkeley (2012-2014). Dr. Kouvelas' research focuses on modeling, simulation, optimization and traffic flow control. His work aims to develop real-time solutions based on control theory and operations research methods. The Road Traffic Engineering group develops algorithmic solutions that are components of intelligent transportation systems used in traffic control centers. Recent technological advances in autonomous vehicles have expanded their research topics as the industry seeks efficient operational solutions for autonomous mobility. They are particularly interested in extending their work to the design of advanced management strategies for urban networks that utilize connected vehicles to improve traffic operations and develop network-wide control strategies that minimize environmental impacts. His recent publications (2023-2025) demonstrate strong focus on traffic prediction using deep learning techniques, bike lane allocation impacts on urban networks, transit network resilience against disruptions, vehicle trajectory extraction from aerial recordings, and traffic control for mixed traffic systems with connected and autonomous vehicles. His work bridges theoretical developments in control theory with practical traffic engineering challenges. Scientific Awards No specific scientific awards were mentioned in the provided information. Advising and Grants Dr. Kouvelas supervises PhD and Master's students in traffic engineering and intelligent transportation systems. His research is supported by various grants including a grant from the Hong Kong Research Grant Council (Grant No. GRF 11216323) for research on traffic speed prediction. Laboratories and Teams Dr. Kouvelas leads the multidisciplinary Road Traffic Engineering research group at IVT, which consists of researchers with backgrounds in civil engineering, electrical engineering, mechanical engineering, computer science, control, and operations research. The group's work spans multiple areas including traffic flow theory, traffic operations, connected and automated vehicles, and intelligent transportation systems.